HUP-BMA: An Integration of Hydrologic Uncertainty Processor and Bayesian Model Averaging for Streamflow Forecasting

Coulibaly, Paulin and Darbandsari, Pedram, (2021). HUP-BMA: An Integration of Hydrologic Uncertainty Processor and Bayesian Model Averaging for Streamflow Forecasting. Water Resources Research, 57(10), n/a-n/a

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  • Sub-type Journal article
    Author Coulibaly, Paulin
    Darbandsari, Pedram
    Title HUP-BMA: An Integration of Hydrologic Uncertainty Processor and Bayesian Model Averaging for Streamflow Forecasting
    Appearing in Water Resources Research
    Volume 57
    Issue No. 10
    Publication Date 2021-09-29
    Place of Publication Washington D.C.
    Publisher American Geophysical Union
    Start page n/a
    End page n/a
    Language eng
    Abstract Uncertainty quantification and providing probabilistic streamflow forecasts are of particular interest for water resource management. The hydrologic uncertainty processor (HUP) is a well-known Bayesian approach used to quantify hydrologic uncertainty based on observations and deterministic forecasts. This uncertainty quantification is model-specific; however, utilizing information from multiple hydrologic models should be advantageous and should lead to better probabilistic forecasts. Using seven, structurally different, conceptual models, this study first aims at evaluating the effects of implementing different hydrologic models on HUP performance. Second, using the concepts of the Bayesian Model Averaging (BMA) approach, a multimodel HUP-based Bayesian postprocessor (HUP-BMA) is proposed where the combination of posterior distributions derived from HUP with different hydrologic models are used to better quantify the hydrologic uncertainty. All postprocessing approaches are applied for medium-range daily streamflow forecasting (1–14 days ahead) in two watersheds located in Ontario, Canada. The results indicate that the HUP forecasts for short lead-times are negligibly affected by implementing different hydrologic models, while with increasing lead-time and flow magnitude, they significantly depend on the quality of the deterministic forecast. Moreover, the superiority of the proposed HUP-BMA method over HUP is demonstrated based on various verification metrics in both watersheds. Additionally, HUP-BMA outperformed the original BMA in quantifying hydrologic uncertainty for short lead-times. However, by increasing lead-time, considering the effects of initially observed flow on HUP-BMA formulation may not be beneficial. So, its modified version unconditioned on initial observations is preferred.
    Copyright Holder American Geophysical Union
    Copyright Year 2021
    Copyright type Creative commons
    DOI 10.1029/2020WR029433
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    Created: Sat, 29 Jan 2022, 06:51:46 JST by Anderson, Kelsey on behalf of UNU INWEH